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Abstract:

In the real world concepts are often not stable but change with time. A
typical example of this in the medical context is antibiotic resistance, where pathogen
sensitivity may change over time as new pathogen strains develop resistance to
antibiotics which were previously effective. This problem, known as concept drift,
complicates the task of learning a model from medical data and requires special approaches,
different from commonly used techniques, which treat arriving instances
as equally important contributors to the final concept. The underlying data distribution
may change as well, making previously built models useless, which is known
as virtual concept drift. These changes make regular updates of the model necessary.
Among the most popular and effective approaches to handle concept drift is
ensemble learning, where a set of models built over different time periods is maintained
and the best model is selected or the predictions of models are combined according
to their expertise level regarding the current concept. In this paper we propose
a new ensemble integration technique that helps to better track concept drift at
the instance level. Our experiments with the antibiotic resistance data show that
dynamic integration of classifiers built over small time intervals can be more effective
than the best single learning algorithm applied in combination with feature selection,
which gives the best known accuracy for the considered problem domain.
Besides, dynamic integration is significantly better than weighted voting which is
currently the most commonly used integration approach for tracking concept drift
with ensembles.